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Predicting PBM binding from HT-SELEX data Workshop Project

Predicting PBM binding from HT-SELEX data Workshop Project. Yaron Orenstein 22 October 2013. Outline. 1. Some background again… 2. The project. 1. Background. Slides with Ron Shamir and Chaim Linhart. Gene: from DNA to protein. Pre-mRNA. Mature mRNA. DNA. protein. transcription.

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Predicting PBM binding from HT-SELEX data Workshop Project

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  1. Predicting PBM binding from HT-SELEX dataWorkshop Project Yaron Orenstein 22 October 2013

  2. Outline 1. Some background again… 2. The project

  3. 1. Background Slides with Ron Shamir and Chaim Linhart

  4. Gene: from DNA to protein Pre-mRNA Mature mRNA DNA protein transcription splicing translation

  5. DNA • DNA: a “string” over the alphabet of 4 bases (nucleotides): { A, C, G, T } • Resides in chromosomes • Complementary strands: A-T ; C-G • Forward/sense strand: AACTTGCG • Reverse-complement/anti-sense strand: TTGAACGC • Directional: from 5’ to 3’: • (upstream) AACTTGCGATACTCCTA (downstream) 5’ end 3’ end

  6. Gene structure (eukaryotes) Promoter DNA Coding strand Transcription start site (TSS) Transcription (RNA polymerase) Pre-mRNA Intron Exon Exon Splicing (spliceosome) 5’ UTR 3’ UTR Mature mRNA Stop codon Start codon Coding region Translation (ribosome) Protein

  7. Translation • Codon - a triplet of bases, codes a specific amino acid (except the stop codons); many-to-1 relation • Stop codons - signal termination of the protein synthesis process http://ntri.tamuk.edu/cell/ribosomes.html

  8. Genome sequences • Many genomes have been sequences, including those of viruses, microbes, plants and animals. • Human: • 23 pairs of chromosomes • 3+ Gbps (bps = base pairs) , only ~3% are genes • ~25,000 genes • Yeast: • 16 chromosomes • 20 Mbps • 6,500 genes

  9. Regulation of Expression • Each cell contains an identical copy of the whole genome - but utilizes only a subset of the genes to perform diverse, unique tasks • Most genes are highly regulated – their expression is limited to specific tissues, developmental stages, physiological condition • Main regulatory mechanism – transcriptional regulation

  10. TF TF 5’ 3’ Gene BS BS Transcriptional regulation • Transcription is regulated primarily by transcription factors (TFs) – proteins that bind to DNA subsequences, called binding sites (BSs) • TFBSs are located mainly (not always!) in the gene’s promoter – the DNA sequence upstream the gene’s transcription start site (TSS) • BSs of a particular TF share a common pattern, or motif • Some TFs operate together – TF modules TSS

  11. TFBS motif models - strings AC CG ACT T • Consensus (“degenerate”) string: gene 1 gene 2 AACTGT gene 3 CACTGT gene 4 CACTCT gene 5 CACTGT gene 6 gene 7 gene 8 gene 9 AACTGT gene 10 • List of k-mers (weighted or unweighted).

  12. TFBS models - PWM Position weight matrix (PWM): each position has weights for the 4 possible letters (A, C, G, T). For example: Logo format:

  13. Protein Binding MicroarraysBerger et al, Nat. Biotech 2006 Generate an array of double-stranded DNA with all possible k-mers Detect TF binding to specific k-mers 13

  14. PBM (2) 14

  15. PBM - implementation Use 60-mers (Agilent): 24nt constant primer + 36nt variable region De Bruijn seq of all 10-mers (410 long) split into 36nt long fragments with 9nt overlap ~40K probes 15

  16. High-throughput SELEXZhao, Granas and Stormo, Plos Comp. Bio. 2009Jolma et al, Genome Research 2010Slattery et al, Cell 2011 Start with a pool of random oligos. Repeat: Let the protein bind to the oligos. Filter out bound oligos. Sequence them. Amplify them and set as the new pool of oligos. 16

  17. High-throughput SELEX

  18. The computational challenge • Input: HT-SELEX data (4-6 sequence files) of one TF and a list of PBM probes (1 sequence file). • Goal: Rank PBM probes according to binding intensity. • Intuition: learning a binding model in one technology to predict binding in another.

  19. The project

  20. General goals • Research - Learn about known solutions - Trial and error with training data • Develop software from A-Z: • Design • Implementation (Optimization) • Execution & analysis of test data • A taste of bioinformatics • Have fun • Get credit…

  21. The computational task • Given a set of HT-SELEX data of different TFs. • Learn a binding model for each TF and use it to rank PBM probes. • Main challenges: • Performance (time, memory) • Accuracy

  22. HT-SELEX Input • 4-6 sequence files with hundred of thousands of lines, each containing oligo sequence and its number of occurrences. <sequence 14/20/30/40 bp> \t <count> \n Cycle 1 Cycle 3 Cycle 2 Cycle 0

  23. PBM Input File with ~41K lines, each containing a probe sequence of length 36. <sequence 36bp> \n • The training file will be sorted according to binding intensity. • The output is a file with the same sequences, only sorted.

  24. Input schedule You will be given: Week 1: 50 training sets (HT-SELEX data + sorted PBM probes data). Week 8: 50 test1 sets (HT-SELEX data + unsorted PBM file). You have to sort the PBM probes. Week 13: 50 test2 sets (HT-SELEX data + unsorted PBM file). You have to sort the PBM probes. Week 13: In the final project presentation, you will be given 12 online test sets and your software will be applied to it.

  25. Output • A sorted PBM file –same sequences as in the input, only sorted. • A logo format of your model (i.e. displayed on the screen). The file logo.zip contains a java package with the code that will easily display your motif. bits = 2 - entropy

  26. Ranking k-mers • One possible way to start: rank the k-mers in some way. Scores for example: 1. Frequency in some cycle. 2. Ratio: freq. in cycle i / freq. in cycle (i-1). • You can think of other scores that incorporate more information, aggregate cycles, correct for biases. • This is just an example. You can think of other ways to start.

  27. Alignment procedure • Then, you can align the significant k-mers. • You may take into account the relative score. • Don’t forget about the reverse complement! • Example: Cebpb TF

  28. Deciding the length of the motif Another challenge is to decide the length of the motif. Most binding site are 6-12 bp long. You should consider the information each position contains and decide on the length accordingly. Consider also the read coverage of the experiment.

  29. The goal • To rank high the top 100 PBM probes in the PBM file (= positive probes). Return a file with all PBM probes ranked. • For a point in the ranked list we can define: • Precision = (# positives above the point) / (location of point) • Recall = (# positive above the points) / (# positives)

  30. AUC of Precision-Recall Precision = # positives above the point / location of point Recall = # positive above the point / # positives PR curve = move the threshold over the list, each time calculating new precision and recall (the points of the curve). AUC = area under the curve.

  31. Scoring PBM probes • Several scores are available, e.g. score each k-mer and take maximum/sum. • Scoring a k-mer according to a model: • PWM: multiply probabilities. • K-mers: assign the value accordingly. • You can suggest new scores and models.

  32. Implementation • Java (Eclipse) ; Linux (Other languages are possible, but will not participate in bonus). • Input: the 1st argument is the PBM filename, and 4-6 filenames of SELEX files. • Output: 1) ranked PBM file; 2) model presented in logo format. • A package for motif logo will be supplied. • Time performance will be measured. • Reasonable documentation. • Separate packages for data-structures, scores, GUI, I/O, etc.

  33. Submission • Printed design document. • Printed code – for comments and remarks. • Printed results document – for each test set the model in logo format. • 50 ranked PBM files, e.g. TF_32.pbm (submitted by email) (for test1 and test2, separately). • Executable for the online test.

  34. Grade • 15% for the design • 25% for the implementation (10% for modularity, clarity, documentation, f(r,k)*15% for efficiency) • 20% for the final report and presentation • f(r,k)*50% for the accuracy of the test results • f(r,k)*15% for test 1 • f(r,k)*20% for test 2 • f(r,k)*15% for test 3 • Where • r = group’s rank in test out of k groups (top rank r=k) • f(r,k) = 0.5+0.5*r/k • So a uniformly top ranking group can get 110, and uniformly least ranking can get 82. • Ties will be scored לבית הילל

  35. Schedule • First progress report 19/11 (meetings) • Test1 10/12 (submission) • Design document 24/12 (submission) • Test2 + executable 14/1 (submission) • Final presentation 18/2 (meeting) • We shall meet with each group on the meetings dates – mark your calendars! • Schedule can be made earlier if you are ready. • You are always welcome to meet us. Contact us by email.

  36. Design document • Due in week 10 (24/12). • 3-5 pages (Word), Hebrew/English • Briefly describe main goal, input and output of program • Describe main data structures, algorithms, and scores. • Meet with me before submission.

  37. References HT-SELEX: • Zhao Y, Granas D and Stormo GD. Inferring binding energies from selected binding sites. PLoS Computational Biology. 2009;5(12):e1000590. • Jolma A, Kivioja T, Toivonen J, Cheng L, Wei GH, Enge M, Taipale M, Vaquerizas JM, Yan J, Sillanpaa MJ, Bonke M, Palin K, Talukder S, Hughes TR, Luscombe NM, Ukkonen E and Taipale J. Multiplexed massively parallel SELEX for characterization of human transcription factor binding specificities. Genome Research. 2010;20:861-873 • Slattery M, Riley T, Liu P, Abe N, Gomez-Alcala P, Dror I, Zhou T, Rohs R, Honig B, Bussemaker HJ and Mann RS. Cofactor binding evokes differences in DNA binding specificity between Hox proteins. Cell. 2011;147:1270-1282. PBM: • Berger MF, Philippakis AA, Quershi AM, He FS, EstepIII PW, Bulyk ML. Compact, universal DNA microarrays to comprehensively determine transcription-factor binding site specificities. Nature biotechnology. 2006;338:1429-1435.

  38. Fin

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